Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins

Ha Yun Lee, Eunhee G. Kim, Hye Ryeon Jung, Jin Woo Jung, Han Byeol Kim, Jin Won Cho, Kristine M. Kim, Eugene C. Yi

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14 Citations (Scopus)


Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of spectral count in the MS data-dependent acquisition, however, inherently introduces the spectral count variation for low-abundance proteins in multiplicative LC-MS/MS analysis, which hampers sensitive proteome quantification. As many low-abundance proteins play important roles in cellular processes, deducing low-abundance proteins in a quantitatively reliable manner greatly expands the depth of biological insights. Here, we implemented the Moment Adjusted Imputation error model in the spectral count refinement as a post PLGEM-STN for improving sensitivity for quantitation of low-abundance proteins by reducing spectral count variability. The statistical framework, automated spectral count refinement by integrating the two statistical tools, was tested with LC-MS/MS datasets of MDA-MB468 breast cancer cells grown under normal and glucose deprivation conditions. We identified about 30% more quantifiable proteins that were found to be low-abundance proteins, which were initially filtered out by the PLGEM-STN analysis. This newly developed statistical framework provides a reliable abundance measurement of low-abundance proteins in the spectral count-based label-free proteome quantification and enabled us to detect low-abundance proteins that could be functionally important in cellular processes.

Original languageEnglish
Article number13653
JournalScientific reports
Issue number1
Publication statusPublished - 2019 Dec 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (NRF-2016R1A5A1010764 and NRF-2015M3A9B6073835 to ECY; NRF-2016R1D1A1B04931656 and NRF-2011-0025320 to KMK) and Global Infrastructure Program through the NRF funded by the Ministry of Science and ICT (NRF-2017K1A3A1A19071651 to ECY).

Publisher Copyright:
© 2019, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


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